This paper brings forward the question of which acoustic features are
the most adequate for identifying beats computationally in acoustic
music pieces. We consider many different features computed on
consecutive short portions of acoustic signal, among which those
currently promoted in the literature on beat induction from acoustic
signals and several original features, unmentioned in this literature.
Evaluation of feature sets regarding their ability to provide reliable
cues to the localization of beats is based on a machine learning
methodology with a large corpus of beat-annotated music pieces, in audio
format, covering distinctive music categories.
Confirming common knowledge, energy is shown to be a very relevant cue
to beat induction (especially the temporal variation of energy in
various frequency bands, with the special relevance of frequency bands
below 500 Hz and above 5 kHz). Some of the new features proposed in this
paper are shown to outperform features currently promoted in the
literature on beat induction from acoustic signals. We finally
hypothesize that modelling beat induction may involve many different,
complementary, acoustic features and that the process of selecting
relevant features should partly depend on acoustic properties of the
very signal under consideration.